If you’re building on AWS with Postgres, there’s a moment most engineering teams hit. Data grows, query patterns shift from point lookups to scans and aggregates across millions of rows, and vanilla Postgres starts showing its limits. The typical answer is a second system: ClickHouse, Redshift, or a warehouse. That path has a cost: pipelines to build and maintain, lag between your transactional data and your analytics output, and two systems to operate instead of one. Tiger Data extends Postgres with time-series primitives, columnar storage, and lifecycle automation so analytical workloads stay fast and correct on your live data. No second system. No pipeline. No re-architecture. Join our team on-site all day June 10. Book 30 minutes and we'll look at your query patterns, your data growth, and where Postgres is starting to hurt, then show you what a unified Postgres platform on AWS looks like in practice.